simule.nh.MSAR.VM: Simulation of (non) homogeneous Markov Stiwtching autoregressive models von Mises innovations

Description

simule.nh.MSAR.VM simulates realisations of (non) homogeneous Markov Switching autoregressive models with von Mises innovations

Usage

simule.nh.MSAR.VM(theta, Y0, T, N.samples = 1, covar.emis = NULL, covar.trans = NULL)

Arguments

theta

list of class MSAR including model parameters and a description of the model. See init.theta.MSAR.VM for more details.

Initial value. Array of dimension order*N.samples*d with order the AR order, N.samples the number of samples to be simulated and d the dimension of the considered data.

Length of each realisation to be simulated

N.samples

number of samples to be simulated

covar.emis

emission covariate or lag for non homogeneous models. Lag is used if the covariate is the lagged time series.

covar.trans

transition covariate or lag for non homogeneous models. Lag is used if the covariate is the lagged time series.

Value

List including

..$Y

simulated observation time series

..$S

simulated Markov chain

%% ~Describe the value returned %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ...

References

Ailliot P., Bessac J., Monbet V., P\`ene F., (2014) Non-homogeneous hidden Markov-switching models for wind time series. JSPI.

Examples

Run this code

# NOT RUN {
	##Not run
#data(WindDir)
#T = dim(WindDir)[1]
#N.samples = dim(WindDir)[2]
#Y = array(WindDir,c(T,N.samples,1))
# von Mises homogeneous MSAR
#M = 2
#order = 1
#theta.init = init.theta.MSAR.VM(Y,M=M,order=order,label="HH")
#polar.hh = fit.MSAR.VM(Y,theta.init,MaxIter=50,verbose=TRUE,eps=1e-8)

#K.sim = 1
#Y0 = array(Y[1:2,sample(1:N.samples,K.sim,replace=T),],c(2,K.sim,1))
#sim.dir = simule.nh.MSAR.VM(polar.hh$theta,Y0=Y0,T,N.samples=K.sim)

## Not run
#theta.init$mu = polar.hh$theta$mu
# theta.init$kappa = polar.hh$theta$kappa+1i*0 # kappa complex
# theta.init$prior = polar.hh$theta$prior
# theta.init$transmat = polar.hh$theta$transmat
# polar.hh.c = fit.MSAR.VM(Y,theta.init,MaxIter=50,verbose=TRUE,eps=1e-8)
	
# theta.init = init.theta.MSAR.VM(Y,M=M,order=order,label="NH",ncov=1,nh.transitions="VM")
# theta.init$mu = polar.hh.c$theta$mu
# theta.init$kappa = polar.hh.c$theta$kappa # kappa complex
# theta.init$prior = polar.hh.c$theta$prior
# theta.init$transmat = polar.hh.c$theta$transmat
# theta.init$par.trans =  matrix(c(polar.hh.c$theta$mu,.1*matrix(1,M,1)),M,2)+1i
#Y.tmp = array(Y[2:T,,],c(T-1,N.samples,1))
#Z = array(Y[1:(T-1),,],c(T-1,N.samples,1))
# polar.nh.c = fit.MSAR.VM(Y.tmp,theta.init,MaxIter=1,verbose=T,eps=1e-8,covar.trans=Z)
#K.sim = 100
#Y0 = array(Y[1:2,sample(1:N.samples,K.sim,replace=T),],c(2,K.sim,1))
#sim.dir = simule.nh.MSAR.VM(polar.nh.c$theta,Y0=Y0,T,N.samples=K.sim,covar.trans=1)


# }